Cadaveric study investigating the phrenic-sparing volume for anterior suprascapular nerve block
Bibliographic record
Abstract
Background This cadaveric study investigated the maximum effective volume of dye in 90% of cases (MEV90), required to stain the suprascapular nerve while sparing the phrenic nerve during the performance of an anterior suprascapular nerve block. Methods In cadaveric neck specimens, using ultrasound guidance, the block needle was advanced until its tip was positioned underneath the omohyoid muscle next to the suprascapular nerve. The dye was injected in order to achieve circumferential spread around the latter. Successful phrenic-sparing suprascapular nerve block was defined as the non-staining of the phrenic nerve on dissection. Volume assignment was carried out using a Biased Coin Design, whereby the volume of dye administered to each cadaveric specimen depended on the response of the previous one. In case of failure (ie, stained phrenic nerve), the next one received a lower volume (defined as the previous volume with a decrement of 2 mL). If the previous cadaveric specimen had a successful block (ie, non-stained phrenic nerve), the next one was randomized to a higher volume (defined as the previous volume with an increment of 2 mL), with a probability of b=0.11, or the same volume, with a probability of 1 – b=0.89. Results Thirty-one cadavers (56 cadaveric neck specimens) were included in the study. Using isotonic regression and bootstrap CI, the MEV90 for phrenic-sparing anterior suprascapular nerve block was estimated to be 4.2 mL (95% CI 3.0 to 5.0 mL). The probability of a successful response was estimated to be 0.90 (95% CI 0.84 to 0.96). Conclusion For ultrasound-guided anterior suprascapular nerve block, the MEV90 of dye required to spare the phrenic nerve is 4.2 mL. Further studies are required to correlate this finding with the MEV90 of local anesthetic in live subjects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".